SoroushVahidi/scidocs huggingface dataset:
Eccentric training in chronic painful impingement syndrome of the shoulder: results of a pilot study
(2004)

Treatment with painful eccentric muscle training has been demonstrated to give good clinical results in patients with chronic Achilles tendinosis. The pain mechanisms in chronic painful shoulder impingement syndrome have not been scientifically clarified, but the histological changes found in the supraspinatus tendon have similarities with the findings in Achilles tendinosis. In this pilot study, nine patients (five females and four males, mean age 54 years) with a long duration of shoulder pain (mean 41 months), diagnosed as having shoulder impingement syndrome and on the waiting list for surgical treatment (mean 13 months), were included. Patients with arthrosis in the acromio-clavicular joint or with large calcifications causing mechanical impingement during horizontal shoulder abduction were not included. We prospectively studied the effects of a specially designed painful eccentric training programme for the supraspintus and deltoideus muscles (3×15 reps, 2 times/day, 7 days/week, for 12 weeks). The patients evaluated the amount of shoulder pain during horizontal shoulder activity on a visual analogue scale (VAS), and satisfaction with treatment. Constant score was assessed. After 12 weeks of treatment, five patients were satisfied with treatment, their mean VAS had decreased (62–18, P<0.05), and their mean Constant score had increased (65–80, P<0.05). At 52-week follow-up, the same five patients were still satisfied (had withdrawn from the waiting list for surgery), and their mean VAS and Constant score were 31 and 81, respectively. Among the satisfied patients, two had a partial suprasinatus tendon rupture, and three had a Type 3 shaped acromion. In conclusion, the material in this study is small and the follow-up is short, but it seems that although there is a long duration of pain, together with bone and tendon abnormalities, painful eccentric supraspinatus and deltoideus training might be effective. The findings motivate further studies.
A Proximity-Aware Hierarchical Clustering of Faces
(2017)

In this paper, we propose an unsupervised face clustering algorithm called “Proximity-Aware Hierarchical Clustering” (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clus- ters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging un- constrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images.
Touch, Taste, & Smell User Interfaces: The Future of Multisensory HCI
(2016)

The senses we call upon when interacting with technology are very restricted. We mostly rely on vision and audition, increasingly harnessing touch, whilst taste and smell remain largely underexploited. In spite of our current knowledge about sensory systems and sensory devices, the biggest stumbling block for progress concerns the need for a deeper understanding of people's multisensory experiences in HCI. It is essential to determine what tactile, gustatory, and olfactory experiences we can design for, and how we can meaningfully stimulate such experiences when interacting with technology. Importantly, we need to determine the contribution of the different senses along with their interactions in order to design more effective and engaging digital multisensory experiences. Finally, it is vital to understand what the limitations are that come into play when users need to monitor more than one sense at a time. The aim of this workshop is to deepen and expand the discussion on touch, taste, and smell within the CHI community and promote the relevance of multisensory experience design and research in HCI.
Minimum Text Corpus Selection for Limited Domain Speech Synthesis
(2014)

This paper concerns limited domain TTS system based on the concatenative method, and presents an algorithm capable to extract the minimal domainoriented text corpus from the real data of the given domain, while still reaching the maximum coverage of the domain. The proposed approach ensures that the least amount of texts are extracted, containing the most common phrases and (possibly) all the words from the domain. At the same time, it ensures that appropriate phrase overlapping is kept, allowing to find smooth concatenation in the overlapped regions to reach high quality synthesized speech. In addition, several recommendations allowing a speaker to record the corpus more fluently and comfortably are presented and discussed. The corpus building is tested and evaluated on several domains differing in size and nature, and the authors present the results of the algorithm and demonstrate the advantages of using the domain oriented corpus for speech synthesis.
BreakDancer: An algorithm for high resolution mapping of genomic structural variation
(2009)

Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancer's performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.
ADDITIVE LOGISTIC REGRESSION : A STATISTICAL VIEW OF BOOSTING
(1998)

Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance. We show that this seemingly mysterious phenomenon can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multiclass generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multiclass generalizations of boosting in most situations, and far superior in some. We suggest a minor modification to boosting that can reduce computation, often by factors of 10 to 50. Finally, we apply these insights to produce an alternative formulation of boosting decision trees. This approach, based on best-first truncated tree induction, often leads to better performance, and can provide interpretable descriptions of the aggregate decision rule. It is also much faster computationally, making it more suitable to large-scale data mining applications.
Sharing Visual Features for Multiclass and Multiview Object Detection
(2004)

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection
A Hybrid Discriminative/Generative Approach for Modeling Human Activities
(2005)

Accurate recognition and tracking of human activities is an important goal of ubiquitous computing. Recent advances in the development of multi-modal wearable sensors enable us to gather rich datasets of human activities. However, the problem of automatically identifying the most useful features for modeling such activities remains largely unsolved. In this paper we present a hybrid approach to recognizing activities, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities. We tested the activity recognition system using over 12 hours of wearable-sensor data collected by volunteers in natural unconstrained environments. The models succeeded in identifying a small set of maximally informative features, and were able identify ten different human activities with an accuracy of 95%.
New Hash Functions and Their Use in Authentication and Set Equality
(1981)

In this paper we exhibit several new classes of hash functions with certain desirable properties, and introduce two novel applications for hashing which make use of these functions. One class contains a small number of functions, yet is almost universal,. If the functions hash n-bit long names into m-bit indices, then specifying a member of the class requires only O((m + log, log,(n)) . log,(n)) bits as compared to O(n) bits for earlier techniques. For long names, this is about a factor of m larger than the lower bound of m + log, n -log, m bits. An application of this class is a provably secure authentication technique for sending messages over insecure lines. A second class of functions satisfies a much stronger property than universal,. We present the application of testing sets for equality. The authentication technique allows the receiver to be certain that a message is genuine. An “enemy”-even one with infinite computer resources-cannot forge or modify a message without detection. The set equality technique allows operations including “add member to set,” “delete member from set” and “test two sets for equality” to be performed in expected constant time and with less than a specified probability of error.
The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials
(2017)

Physically-active video games ('exergames') have recently gained popularity for leisure and entertainment purposes. Using exergames to combine physical activity and cognitively-demanding tasks may offer a novel strategy to improve cognitive functioning. Therefore, this systematic review and meta-analysis was performed to establish effects of exergames on overall cognition and specific cognitive domains in clinical and non-clinical populations. We identified 17 eligible RCTs with cognitive outcome data for 926 participants. Random-effects meta-analyses found exergames significantly improved global cognition (g=0.436, 95% CI=0.18-0.69, p=0.001). Significant effects still existed when excluding waitlist-only controlled studies, and when comparing to physical activity interventions. Furthermore, benefits of exergames where observed for both healthy older adults and clinical populations with conditions associated with neurocognitive impairments (all p<0.05). Domain-specific analyses found exergames improved executive functions, attentional processing and visuospatial skills. The findings present the first meta-analytic evidence for effects of exergames on cognition. Future research must establish which patient/treatment factors influence efficacy of exergames, and explore neurobiological mechanisms of action.
Attitudes -1 The Construction of Attitudes
(2002)

Attitudes have long been considered a central concept of social psychology. In fact, early writers have defined social psychology as the scientific study of attitudes (e.g., Thomas & Znaniecki, 1918) and in 1954 Gordon Allport noted, "This concept is probably the most distinctive and indispensable concept in contemporary American social psychology" (p. 43). As one may expect of any concept that has received decades of attention, the concept of attitudes has changed over the years (see Allport, 1954, for an early review). The initial definitions were broad and encompassed cognitive, affective, motivational, and behavioral components. For example, Allport (1935) defined an attitude as "a mental and neural state of readiness, organized through experience, exerting a directive and dynamic influence upon the individual's response to all objects and situations with which it is related" (p. 810). A decade later, Krech and Crutchfield (1948) wrote, "An attitude can be defined as an enduring organization of motivational, emotional, perceptual, and cognitive processes with respect to some aspect of the individual's world" (p. 152). These definitions emphasized the enduring nature of attitudes and their close relationship to individuals' defined attitudes simply in terms of the probability that a person will show a specified behavior in a specified situation. In subsequent decades, the attitude concept lost much of its breadth and was largely reduced to its evaluative component. In the succinct words of Daryl Bem, "Attitudes are likes and dislikes" attitudes as "a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor" (p. 1). Along the way, many functions that were initially ascribed to attitudes have been reassigned to other cognitive structures and the accumulating body of empirical findings drew many of the classic assumptions into question. A growing body of literature suggests that attitudes may be much less enduring and stable than has traditionally been assumed. As we review below, self-reports of attitudes are highly context-dependent and can be profoundly influenced by minor changes in question wording, question format or question order. Attitudes-3 their assessment is subject to contextual influences. For other researchers, the same findings indicate that all we assess in attitude measurement are evaluative judgments that respondents construct at the time they are asked, based on whatever information happens to be accessible (e.g., Schwarz & Strack, 1991). From this perspective, the traditional attitude concept may not be particularly useful and we may learn more …
Towards Operational Measures of Computer Security
(1993)

Ideally, a measure of the security of a system should capture quantitatively the intuitive notion of ‘the ability of the system to resist attack’. That is, it should be operational, reflecting the degree to which the system can be expected to remain free of security breaches under particular conditions of operation (including attack). Instead, current security levels at best merely reflect the extensiveness of safeguards introduced during the design and development of a system. Whilst we might expect a system developed to a higher level than another to exhibit ‘more secure behaviour’ in operation, this cannot be guaranteed; more particularly, we cannot infer what the actual security behaviour will be from knowledge of such a level. In the paper we discuss similarities between reliability and security with the intention of working towards measures of ‘operational security’ similar to those that we have for reliability of systems. Very informally, these measures could involve expressions such as the rate of occurrence of security breaches (cf rate of occurrence of failures in reliability), or the probability that a specified ‘mission’ can be accomplished without a security breach (cf reliability function). This new approach is based on the analogy between system failure and security breach. A number of other analogies to support this view are introduced. We examine this duality critically, and have identified a number of important open questions that need to be answered before this quantitative approach can be taken further. The work described here is therefore somewhat tentative, and one of our major intentions is to invite discussion about the plausibility and feasibility of this new approach. Towards Operational Measures of Computer Security 2 _____________________________________________________________________
Estimation of ECG features using LabVIEW
(2009)

320 Abstract—Various methods have been proposed and used for ECG feature extraction with considerable percentage of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs. In this work we have developed an algorithm based on wavelet transform approach using LabVIEW 8.6. LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a graphical programming language that uses icons instead of lines of text to create programs. Unlike text based programming language, LabVIEW uses the data flow programming, where the flow of data determines execution. The flexibility, modular nature and ease to use programming possible with labview, makes it less complex. The proposed algorithm is executed in two stages. Firstly, it preprocesses (denoises) the signal to remove the noise from the ECG signal. Then it detects P-wave, T-wave, QRS complex and their onsets and offsets. LabVIEW and the related toolkits, advanced signal processing toolkit and math script are used to build the graphical programme for both the stages. The algorithm is evaluated with ECG data files, with different ECG morphologies and noise content taken from CSE multilead data base which contains 5000 samples per ECG, recorded at a sampling frequency of 500Hz.
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
(2016)

Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models can be conducted using a carefully crafted particle MCMC algorithm. This scheme is computationally efficient and yet allows for a fully Bayesian treatment of the problem. Compared to conventional system identification tools or existing learning methods, we show competitive performance and reliable quantification of uncertainties in the model.
Source Code Analysis: A Road Map
(2007)

The automated and semi-automated analysis of source code has remained a topic of intense research for more than thirty years. During this period, algorithms and techniques for source-code analysis have changed, sometimes dramatically. The abilities of the tools that implement them have also expanded to meet new and diverse challenges. This paper surveys current work on source-code analysis. It also provides a road map for future work over the next five-year period and speculates on the development of source-code analysis applications, techniques, and challenges over the next 10, 20, and 50 years.
Efficient 3D Object Segmentation from Densely Sampled Light Fields with Applications to 3D Reconstruction
(2016)

Precise object segmentation in image data is a fundamental problem with various applications, including 3D object reconstruction. We present an efficient algorithm to automatically segment a static foreground object from highly cluttered background in light fields. A key insight and contribution of our article is that a significant increase of the available input data can enable the design of novel, highly efficient approaches. In particular, the central idea of our method is to exploit high spatio-angular sampling on the order of thousands of input frames, for example, captured as a hand-held video, such that new structures are revealed due to the increased coherence in the data. We first show how purely local gradient information contained in slices of such a dense light field can be combined with information about the camera trajectory to make efficient estimates of the foreground and background. These estimates are then propagated to textureless regions using edge-aware filtering in the epipolar volume. Finally, we enforce global consistency in a gathering step to derive a precise object segmentation in both 2D and 3D space, which captures fine geometric details even in very cluttered scenes. The design of each of these steps is motivated by efficiency and scalability, allowing us to handle large, real-world video datasets on a standard desktop computer. We demonstrate how the results of our method can be used for considerably improving the speed and quality of image-based 3D reconstruction algorithms, and we compare our results to state-of-the-art segmentation and multiview stereo methods.
Odin: Team VictorTango's entry in the DARPA Urban Challenge
(2008)

The DARPA Urban Challenge required robotic vehicles to travel more than 90 km through an urban environment without human intervention and included situations such as stop intersections, traffic merges, parking, and roadblocks. Team VictorTango separated the problem into three parts: base vehicle, perception, and planning. A Ford Escape outfitted with a custom drive-by-wire system and computers formed the basis for Odin. Perception used laser scanners, global positioning system, and a priori knowledge to identify obstacles, cars, and roads. Planning relied on a hybrid deliberative/reactive architecture to
The Skiplist-Based LSM Tree
(2018)

Log-Structured Merge (LSM) Trees provide a tiered data storage and retrieval paradigm that is aractive for write-optimized data systems. Maintaining an ecient buer in memory and deferring updates past their initial write-time, the structure provides quick operations over hot data. Because each layer of the structure is logically separate from the others, the structure is also conducive to opportunistic and granular optimization. In this paper, we introduce the SkiplistBased LSM Tree (sLSM), a novel system in which the memory buer of the LSM is composed of a sequence of skiplists. We develop theoretical and experimental results that demonstrate that the breadth of tuning parameters inherent to the sLSM allows it broad exibility for excellent performance across a wide variety of workloads.